{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AI第四周作业——聚类、降维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入需要引用的类\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cluster import MiniBatchKMeans\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提取Events文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>start_time</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip</th>\n",
       "      <th>country</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>c_1</th>\n",
       "      <th>...</th>\n",
       "      <th>c_92</th>\n",
       "      <th>c_93</th>\n",
       "      <th>c_94</th>\n",
       "      <th>c_95</th>\n",
       "      <th>c_96</th>\n",
       "      <th>c_97</th>\n",
       "      <th>c_98</th>\n",
       "      <th>c_99</th>\n",
       "      <th>c_100</th>\n",
       "      <th>c_other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>684921758</td>\n",
       "      <td>3647864012</td>\n",
       "      <td>2012-10-31T00:00:00.001Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>244999119</td>\n",
       "      <td>3476440521</td>\n",
       "      <td>2012-11-03T00:00:00.001Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3928440935</td>\n",
       "      <td>517514445</td>\n",
       "      <td>2012-11-05T00:00:00.001Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2582345152</td>\n",
       "      <td>781585781</td>\n",
       "      <td>2012-10-30T00:00:00.001Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1051165850</td>\n",
       "      <td>1016098580</td>\n",
       "      <td>2012-09-27T00:00:00.001Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 110 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     event_id     user_id                start_time city state  zip country  \\\n",
       "0   684921758  3647864012  2012-10-31T00:00:00.001Z  NaN   NaN  NaN     NaN   \n",
       "1   244999119  3476440521  2012-11-03T00:00:00.001Z  NaN   NaN  NaN     NaN   \n",
       "2  3928440935   517514445  2012-11-05T00:00:00.001Z  NaN   NaN  NaN     NaN   \n",
       "3  2582345152   781585781  2012-10-30T00:00:00.001Z  NaN   NaN  NaN     NaN   \n",
       "4  1051165850  1016098580  2012-09-27T00:00:00.001Z  NaN   NaN  NaN     NaN   \n",
       "\n",
       "   lat  lng  c_1   ...     c_92  c_93  c_94  c_95  c_96  c_97  c_98  c_99  \\\n",
       "0  NaN  NaN    2   ...        0     1     0     0     0     0     0     0   \n",
       "1  NaN  NaN    2   ...        0     0     0     0     0     0     0     0   \n",
       "2  NaN  NaN    0   ...        0     0     0     0     0     0     0     0   \n",
       "3  NaN  NaN    1   ...        0     0     0     0     0     0     0     0   \n",
       "4  NaN  NaN    1   ...        0     0     0     0     0     0     0     0   \n",
       "\n",
       "   c_100  c_other  \n",
       "0      0        9  \n",
       "1      0        7  \n",
       "2      0       12  \n",
       "3      0        8  \n",
       "4      0        9  \n",
       "\n",
       "[5 rows x 110 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events = pd.read_csv(\"events.csv\")\n",
    "events.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>event</th>\n",
       "      <th>invited</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>interested</th>\n",
       "      <th>not_interested</th>\n",
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       "  </thead>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3044012</td>\n",
       "      <td>3072478280</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-10-02 15:53:05.754000+00:00</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "      user       event  invited                         timestamp  interested  \\\n",
       "0  3044012  1918771225        0  2012-10-02 15:53:05.754000+00:00           0   \n",
       "1  3044012  1502284248        0  2012-10-02 15:53:05.754000+00:00           0   \n",
       "2  3044012  2529072432        0  2012-10-02 15:53:05.754000+00:00           1   \n",
       "3  3044012  3072478280        0  2012-10-02 15:53:05.754000+00:00           0   \n",
       "4  3044012  1390707377        0  2012-10-02 15:53:05.754000+00:00           0   \n",
       "\n",
       "   not_interested  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"train.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>event</th>\n",
       "      <th>invited</th>\n",
       "      <th>timestamp</th>\n",
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      ],
      "text/plain": [
       "      user       event  invited                         timestamp\n",
       "0  1776192  2877501688        0  2012-11-30 11:39:01.230000+00:00\n",
       "1  1776192  3025444328        0  2012-11-30 11:39:01.230000+00:00\n",
       "2  1776192  4078218285        0  2012-11-30 11:39:01.230000+00:00\n",
       "3  1776192  1024025121        0  2012-11-30 11:39:01.230000+00:00\n",
       "4  1776192  2972428928        0  2012-11-30 11:39:21.985000+00:00"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv(\"test.csv\")\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#获得只在train和test中出现的event\n",
    "common_event = []\n",
    "isEventExist = False\n",
    "\n",
    "for i in range(len(train)):\n",
    "    for j in range(len(test)):\n",
    "        if train.at[i,'event'] == test.at[j,'event']:\n",
    "            for k in range(len(common_event)):\n",
    "                if(common_event[k] == train.at[i,'event']):\n",
    "                    isEventExist = True\n",
    "                    break\n",
    "            if(isEventExist):\n",
    "                isEventExist = False\n",
    "                break\n",
    "            else:\n",
    "                common_event.append(train.at[i,'event'])\n",
    "                #print('event:', train.at[i,'event'])         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    }
   ],
   "source": [
    "new_events = []\n",
    "#由于events的数据量为300W，数据量较大，这里events的数据取3W\n",
    "for i in range(30000):\n",
    "    for j in range(len(common_event)):\n",
    "        if(events.at[i, 'event_id'] == common_event[j]):\n",
    "            new_events.append(events.loc[i,:])\n",
    "    #打印读取进度\n",
    "    print('%.5f' % (i/30000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将train和test共有的events提取并保存\n",
    "\n",
    "#转换为numpy数组\n",
    "new_events_arr = np.array(new_events)\n",
    "#提取events的列名\n",
    "columns = events.columns.values.tolist()\n",
    "#创建dataframe\n",
    "new_events_df = pd.DataFrame(columns = columns, data = new_events_arr)\n",
    "#保存events文件\n",
    "new_events_df.to_csv('new_event.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 进行K_mean聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   c_1  c_2  c_3  c_4  c_5  c_6  c_7  c_8  c_9  c_10   ...     c_92  c_93  \\\n",
       "0    0    0    0    0    0    0    0    0    0     0   ...        0     0   \n",
       "1    0    0    0    0    0    0    0    0    0     0   ...        0     0   \n",
       "2    0    0    0    3    0    0    0    0    0     0   ...        0     0   \n",
       "3    2    0    0    0    0    0    0    1    0     0   ...        0     0   \n",
       "4    2    1    6    2    2    1    1    3    0     0   ...        1     0   \n",
       "\n",
       "   c_94  c_95  c_96  c_97  c_98  c_99  c_100  c_other  \n",
       "0     0     0     0     0     0     0      0       12  \n",
       "1     0     0     0     0     0     0      0       22  \n",
       "2     0     0     0     0     0     0      0       38  \n",
       "3     0     0     0     0     0     0      0        6  \n",
       "4     0     0     0     0     0     0      0       39  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = pd.read_csv(\"new_event.csv\")\n",
    "X_train = X_train.iloc[:,10:111]\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\YuGo\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# 将训练集合拆分成训练集和校验集\n",
    "X_train_part, X_val = train_test_split(X_train, train_size = 0.8, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 一个参数点（聚类数据为K）的模型，在校验集上评价聚类算法性能\n",
    "def K_cluster_analysis(K, X_train, X_val):\n",
    "    start = time.time()\n",
    "    \n",
    "    print(\"K-means begin with clusters: {}\".format(K));\n",
    "    \n",
    "    #K-means,在训练集上训练\n",
    "    mb_kmeans = MiniBatchKMeans(n_clusters = K)\n",
    "    mb_kmeans.fit(X_train)\n",
    "    \n",
    "    # 在训练集和测试集上测试\n",
    "    #y_train_pred = mb_kmeans.fit_predict(X_train)\n",
    "    y_val_pred = mb_kmeans.predict(X_val)\n",
    "    \n",
    "    #以前两维特征打印训练数据的分类结果\n",
    "    #plt.scatter(X_train[:, 0], X_train[:, 1], c=y_pred)\n",
    "    #plt.show()\n",
    "\n",
    "    # K值的评估标准\n",
    "    #常见的方法有轮廓系数Silhouette Coefficient和Calinski-Harabasz Index\n",
    "    #这两个分数值越大则聚类效果越好\n",
    "    #CH_score = metrics.calinski_harabaz_score(X_train,mb_kmeans.predict(X_train))\n",
    "    CH_score = metrics.silhouette_score(X_train,mb_kmeans.predict(X_train))\n",
    "    \n",
    "    #也可以在校验集上评估K\n",
    "    #v_score = metrics.v_measure_score(y_val, y_val_pred)\n",
    "    \n",
    "    end = time.time()\n",
    "    print(\"CH_score: {}, time elaps:{}\".format(CH_score, int(end-start)))\n",
    "    #print(\"v_score: {}\".format(v_score))\n",
    "    \n",
    "    return CH_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-means begin with clusters: 10\n",
      "CH_score: 0.5570679060849634, time elaps:0\n",
      "K-means begin with clusters: 20\n",
      "CH_score: 0.2944491456845828, time elaps:0\n",
      "K-means begin with clusters: 30\n",
      "CH_score: 0.25469843858472907, time elaps:0\n",
      "K-means begin with clusters: 40\n",
      "CH_score: 0.21649965490416462, time elaps:0\n",
      "K-means begin with clusters: 50\n",
      "CH_score: 0.17947773269562087, time elaps:0\n",
      "K-means begin with clusters: 60\n",
      "CH_score: 0.16924523686943918, time elaps:0\n",
      "K-means begin with clusters: 70\n",
      "CH_score: 0.13333333333333333, time elaps:0\n"
     ]
    }
   ],
   "source": [
    "# 设置超参数（聚类数目K）搜索范围\n",
    "Ks = [10, 20, 30, 40, 50, 60, 70]\n",
    "CH_scores = []\n",
    "for K in Ks:\n",
    "    ch = K_cluster_analysis(K, X_train_part, X_val)\n",
    "    CH_scores.append(ch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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